Business users today often do their investment business plan modeling using computer spreadsheets, for example, Excel® workbooks, that allow for both hard data numbers to be contained within the cells of the spreadsheet and calculated values, derived from data in other cells, sheets, workbooks, files or locations, to be contained within other cells of the spreadsheet.
In the case of business models relating to complex assets, for example, real estate investments, such models can become highly complex. Moreover, since such investments are often independent of each other both in the sense that the performance of one may be wholly unrelated to the performance of another and further disassociated in the sense that they may be diverse in location and/or type. For example, such investments might be in real estate in different cities, states or countries and/or different types of real-estate, such as single family homes, apartment buildings, commercial office space, shopping centers, malls, parking lots or garages, factory space, etc.
For businesses that hold many such diverse assets, a model might be created for each such asset at different times, for example, prior to acquisition to determine whether to acquire the asset, or upon/after acquisition. In addition, for some assets, some prior data may be available that will allow the business to perform projections under different scenarios relating to that asset. In either case, the analysis will often involve deriving new models for new or prospective assets from existing models for similar or related assets. Users often use spreadsheets for their business models specifically because of the flexibility they provide in this regard. Moreover, it is much more efficient for a user to adapt a preexisting spreadsheet-based model that has been “fine-tuned” over time and based upon experience unique to that asset situation, rather than creating a new one for each new asset.
At times it may further be desirable to perform some analysis using such models, for example, to determine absolute or relative performance, consider “what if” scenarios, or perform tax, profitability or other accounting calculations on some period basis. While this can be dome within a single spreadsheet model, this cannot easily be done across multiple often unique spreadsheet models that collectively model a portfolio of assets. Since spreadsheet models for different assets may be non-standardized (and even non standardize-able) and are often created by different people in different formats/styles or customized for specific assets, it may be difficult, if not impossible, to perform multi-asset analysis without individually (and possibly iteratively) manually going into each model, making changes, seeing the result and potentially using that result to make further adjustments in other models, particularly for highly complex models that may extensively rely upon spreadsheet provided features.
Thus, in order to be able to perform multi-asset analysis, some institutions may require asset modeling to be performed using custom modeling tool systems and interfaces that may mimic some aspects of a spreadsheet workbook to some degree and imposes standardization among the different asset models so that they can be collectively analyzed. In other words, they act like a template to which the user must conform.
This approach is inefficient for several reasons. First, it can entail redundancy in user work in terms of transposing aspects of each model into the modeling tool system. Second, this approach subjects them to the inconvenience of maintaining data at multiple places because, when data in a model changes, that change must now be reflected in the data of the modeling tool system. Third, since such modeling tools are not full spreadsheets they lack some of the spreadsheet functions and capabilities. Even if such modeling tools include processing behind their interface that attempts to replicate spreadsheet functions and capabilities, much is lost because spreadsheets provide an array of features and functionality which are nearly impossible to duplicate in a complicated modeling tool. Finally, the template approach generally precludes users from adding or augmenting what the template can do, leaving them with little to no flexibility for unique or non-standard types of data input or manipulations.
Still further, even if the particular modeling tool enables a user to somehow upload or import data from their models into the modeling tool, because the models can be large and complex, the upload process might take a long time and prevent a user from concurrently using that computer.